Spatial Sequential Recurrent Neural Network for Hyperspectral Image Classification

Xiangrong Zhang*, Yujia Sun, Kai Jiang, Chen Li, Licheng Jiao, Huiyu Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


In hyperspectral image processing, classification is one of the most popular research topics. In recent years, research progress made in deep-learning-based hierarchical feature extraction and classification has shown a great power in many applications. In this paper, we propose a novel local spatial sequential (LSS) method, which is used in a recurrent neural network (RNN). Using this model, we can extract local and semantic information for hyperspectral image classification. First, we extract low-level features from hyperspectral images, including texture and differential morphological profiles. Second, we combine the low-level features together and propose a method to construct the LSS features. Afterwards, we build an RNN and use the LSS features as the input to train the network for optimizing the system parameters. Finally, the high-level semantic features generated by the RNN is fed into a softmax layer for the final classification. In addition, a nonlocal spatial sequential method is presented for the recurrent neural network model (NLSS-RNN) to further enhance the classification performance. NLSS-RNN finds nonlocal similar structures to a given pixel and extracts corresponding LSS features, which not only preserve the local spatial information, but also integrate the information of nonlocal similar samples. The experimental results on three publicly accessible datasets show that our proposed method can obtain competitive performance compared with several state-of-the-art classifiers.

Original languageEnglish
Article number8399509
Pages (from-to)4141-4155
Number of pages15
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number11
Publication statusPublished - 28 Jun 2018

Bibliographical note

Funding Information:
Manuscript received February 16, 2018; revised May 10, 2018; accepted May 28, 2018. Date of publication June 27, 2018; date of current version November 27, 2018. This work was supported in part by the National Natural Science Foundation of China under Grant 61772400, Grant 61501353, Grant 61772399, Grant 91438201, and Grant 61573267 and in part by the joint fund of the Equipment Research of Ministry of Education. The work of H. Zhou was supported in part by the U.K. Engineering and Physical Sciences Research Council under Grant EP/N508664/1, Grant EP/R007187/1, and Grant EP/N011074/1, and in part by the Royal Society-Newton Advanced Fellowship under Grant NA160342. (Corresponding author: Xiangrong Zhang.) X. Zhang, Y. Sun, K. Jiang, and L. Jiao are with the Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an 710071, China (e-mail:,;;;

Publisher Copyright:
© 2018 IEEE.

Copyright 2019 Elsevier B.V., All rights reserved.


  • Deep learning
  • high-level semantic feature
  • hyperspectral image (HSI) classification
  • low-level feature
  • recurrent neural network (RNN)

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science


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